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name: deblur-gan description: "Route DeblurGAN users to data preparation, training, and single-image inference workflows." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

DeblurGAN

DeblurGAN is a PyTorch image deblurring repo for learning a blur-to-sharp mapping from paired data. Use this skill when the task is about preparing paired images, training the conditional GAN, or restoring single images from a checkpoint.

Start here

• Read references/installation.md if the environment is not ready.
• Run scripts/check_deblurgan_env.py --repo-root <local DeblurGAN checkout> --cuda for a CUDA smoke check, or omit --cuda if you only need import inspection.
• Use the sub-skill that matches the workflow:
  • sub-skills/data-preparation/SKILL.md for pair creation and layout checks.
  • sub-skills/training/SKILL.md for optimization, checkpoints, and loss configuration.
  • sub-skills/inference/SKILL.md for restoring images and HTML result export.

What this skill expects

• Python 3.11.
• No packaging metadata is present in this checkout, so dependencies are installed manually. A practical baseline is a CUDA-enabled PyTorch/TorchVision build plus dominate and opencv-python-headless; add ssim only if you want to run the source test.py verbatim and visdom only if you want live training plots. See references/installation.md for copyable commands.
• Training and the perceptual-loss path require a CUDA-enabled PyTorch build.
• Inference and data-preparation can use CPU-only import checks, but the generated wrappers still expect the repo modules to be importable.

Common workflows

Prepare data

Use the data-preparation sub-skill when you have separate blur and sharp image trees or when you need to understand the aligned and single layouts.

Train

Use the training sub-skill when you need to configure content_gan, pix2pix, gan, lsgan, or wgan-gp, create checkpoints, or run a smoke-sized optimization pass.

Restore images

Use the inference sub-skill when you need to load latest_net_G.pth, restore a folder of images, or generate the HTML gallery under results/<name>/<phase>_<epoch>/.

Shared conventions

• Training checkpoints are written under checkpoints/<name>/.
• Inference results are written under results/<name>/<phase>_<which_epoch>/.
• The local metric helpers live in util/metrics.py; the shipped test.py also references an external ssim package, so prefer the bundled inference wrapper.
• The repository's unaligned dataset stub and legacy motion_blur/ helpers are not primary routes for this skill.

Shared references

• references/api-reference.md for verified signatures and object roles.
• references/workflows.md for portable command patterns.
• references/troubleshooting.md for cross-cutting failures and recovery steps.
• references/repo-provenance.md and references/repo-routing-metadata.json for refresh and router metadata.